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vgg16_224.py
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vgg16_224.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class VGG16(nn.Module):
def __init__(self):
super(VGG16, self).__init__()
# 输入图片大小为：3 * 224 * 224
self.conv1_1 = nn.Conv2d(3, 64,
3) # 64 * 222 * 222 (224 - 3 + 2*0)/1 + 1 = 222
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=(1, 1)) # 64 * 222* 222 (222 - 3 + 2*1)/1 + 1 = 222
self.maxpool1 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 64 * 112 * 112 (222 - 2 + 2*1)/2 + 1 = 112
self.conv2_1 = nn.Conv2d(64, 128, 3) # 128 * 110 * 110 (112 - 3 + 2*0)/1 + 1 =110
self.conv2_2 = nn.Conv2d(128, 128, 3, padding=(1, 1)) # 128 * 110 * 110 (110 - 3 + 2*1)/1 + 1 =110
self.maxpool2 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 128 * 56 * 56 (110 - 2 + 2*1)/2 + 1 = 56
self.conv3_1 = nn.Conv2d(128, 256, 3) # 256 * 54 * 54 (56 - 3 + 2*0)/1 + 1 = 54
self.conv3_2 = nn.Conv2d(256, 256, 3, padding=(1, 1)) # 256 * 54 * 54 (54 - 3 + 2*1)/1 + 1 = 54
self.conv3_3 = nn.Conv2d(256, 256, 3, padding=(1, 1)) # 256 * 54 * 54 (54 - 3 + 2*1)/1 + 1 = 54
self.maxpool3 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 256 * 28 * 28 (54 - 2 + 2*1)/2 + 1 = 28
self.conv4_1 = nn.Conv2d(256, 512, 3) # 512 * 26 * 26 (28 - 3 + 2*0)/1 + 1 = 26
self.conv4_2 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 26 * 26 (26 - 3 + 2*1)/1 + 1 = 26
self.conv4_3 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 26 * 26 (26 - 3 + 2*1)/1 + 1 = 26
self.maxpool4 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 512 * 14 * 14 (26 - 2 + 2*1)/2 + 1 = 14
self.conv5_1 = nn.Conv2d(512, 512, 3) # 512 * 12 * 12 (14 - 3 + 2*0)/1 + 1 = 12
self.conv5_2 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 12 * 12 (12 - 3 + 2*1)/1 + 1 = 12
self.conv5_3 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 12 * 12 (12 - 3 + 2*1)/1 + 1 = 12
self.maxpool5 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 512 * 7 * 7 (12 - 2 + 2*1)/2 + 1 =7
# view
self.fc1 = nn.Linear(512 * 7 * 7, 4096) # 512 * 7 * 7 = 25088 ————> 4096
self.fc2 = nn.Linear(4096, 4096) # 4096 ————> 4096
self.fc3 = nn.Linear(4096, 1000) # 4096 ————> 1000
# softmax 1 * 1 * 1000
def forward(self, x):
# x.size(0)即为batch_size
in_size = x.size(0)
out = self.conv1_1(x) # 222
out = F.relu(out)
out = self.conv1_2(out) # 222
out = F.relu(out)
out = self.maxpool1(out) # 112
out = self.conv2_1(out) # 110
out = F.relu(out)
out = self.conv2_2(out) # 110
out = F.relu(out)
out = self.maxpool2(out) # 56
out = self.conv3_1(out) # 54
out = F.relu(out)
out = self.conv3_2(out) # 54
out = F.relu(out)
out = self.conv3_3(out) # 54
out = F.relu(out)
out = self.maxpool3(out) # 28
out = self.conv4_1(out) # 26
out = F.relu(out)
out = self.conv4_2(out) # 26
out = F.relu(out)
out = self.conv4_3(out) # 26
out = F.relu(out)
out = self.maxpool4(out) # 14
out = self.conv5_1(out) # 12
out = F.relu(out)
out = self.conv5_2(out) # 12
out = F.relu(out)
out = self.conv5_3(out) # 12
out = F.relu(out)
out = self.maxpool5(out) # 7
# 展平
out = out.view(in_size, -1)
out = self.fc1(out)
out = F.relu(out)
out = self.fc2(out)
out = F.relu(out)
out = self.fc3(out)
out = F.log_softmax(out, dim=1)
return out